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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. To help you get started with the new API, we have published two Jupyter notebook examples: one for node classification, and one for a link prediction task. Specifically, GraphStorm 0.3

APIs 119
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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 105
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How to decide between Amazon Rekognition image and video API for video moderation

AWS Machine Learning

Amazon Rekognition has two sets of APIs that help you moderate images or videos to keep digital communities safe and engaged. Some customers have asked if they could use this approach to moderate videos by sampling image frames and sending them to the Amazon Rekognition image moderation API.

APIs 77
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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning

For example, the claims processing team established an application inference profile with tags such as dept:claims , team:automation , and app:claims_chatbot. Invoke models with application inference profiles : Converse API : Invokes the model using a specified inference profile for conversational interactions.

APIs 134
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. With this launch, customers can now seamlessly share and access ML models registered in SageMaker Model Registry between different AWS accounts.

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Metrics for evaluating content moderation in Amazon Rekognition and other content moderation services

AWS Machine Learning

In this post, we discuss the key elements needed to evaluate the performance aspect of a content moderation service in terms of various accuracy metrics, and a provide an example using Amazon Rekognition Content Moderation API’s. Let’s explore an example. What to evaluate. Measure model accuracy on images.

Metrics 91
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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning

Dru on the backend decodes log data, deciphers error codes, and invokes API calls to troubleshoot. This approach allowed us to break the problem down into multiple steps: Identify the API route. Generate and invoke private API calls. For example, “Show me my backup failures for the past 72 hours, grouped by server.”

APIs 111